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The way this person is delivering these complicated stuffs regarding Open CV and OT, hats off man for your contribution towards the YouTube Community. Do try to make a playlist or video regarding implementation of GANNs tensorflow models along with Opened CV, like Real Time image to image translation using Cycle GANNs. By the way lots if love from this Subscriber
Thank you so much men! I do appreciate your kindness and efforts for sharing this great script even for free. It helps a lot, especillay for many students who are with empty pockets! Gracias!
Thanks for sharing the knowledge and resources. For the information of the fellow viewers/commenters.. I implemented his code in VS Code in Windows 10. 2 errors and following had to be done as it threw some error when I attempted to run it:1. install "pip install tracker" --> Refresh VS code2. Make sure that the specified folder path of the video and code are same. Files --> Open Folder--> Object-Tracking.
Perfect and much needed video , thank you . Can you PLEASE make a video on camera calibration without using the checker board (I mean by using methods inside opencv like PnP method etc...)
this is a really awesome explanation of each and every step u explained very well. sir if I want further vehicle classification can you please say how I supposed to do it..
Hey,Could we think something different like this , instead of calculating area could it be that we will track only the moving elements and complete our task.I don't know could it be or not because I'm new in python and opencv too .if it can please tell. And thank you for sharing such a nice one project.
Sir, thank you for the video with a detailed explanation.If I want the program to execute on two different videos at a time is that possible.If yes please tell the way
Video Data & Python | What is Video Data? | Getting Setup | Converting Videos | Displaying Video | Video Metadata | Pulling Images | Add Annotations | Saving processed video | Summary
Start | Explainer | PART 1: Building a Data Pipeline | Installing Dependencies | Getting Data from Google Images | Load Data using Keras Utils | PART 2: Preprocessing Data | Scaling Images | Partitioning the Dataset | PART 3: Building the Deep Neural Network | Build the Network | Training the DNN | Plotting Model Performance | PART 4: Evaluating Perofmrnace | Evaluating on the Test Partition | Testing on New Data | PART 5: Saving the Model | Saving the model as h5 file | Wrap Up
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